The standard errors for parameter estimates in linear regression are the
square roots of the diagonal elements of the parameter covariance matrix
Inverse[Transpose[X].X]
where X is the design matrix for the regression. The ith row of the
design matrix contains the values of the basis functions evaluated at
the ith data point.
The standard errors for parameter estimates in nonlinear regression are
the square roots of the diagonal elements of the asymptotic parameter
covariance matrix
Inverse[Transpose[approxX].approxX]
where approxX is an approximate design matrix for the nonlinear model.
The ith row of the approximate design matrix contains the values of the
first derivatives of the model function with respect to each of the
parameters evaluated at the ith data point.
Darren Glosemeyer
Wolfram Research
Seo Ho Youn wrote:
> Hello, all.
>
>
>
> Can I ask how SE (standard error) in LinearRegress and NonlinearRegress is
> defined or calculated in Mathematica for a multi-parameter least-square fit?
> Or, does anybody know about document (or definition) on SE in Mathematica? I
> haven't been able to find any about how it is calculated in Mathematica.
>
>
>
> Thank you for your help and have a good day.
>
>
>
> Seo Ho
>
>